Project Summary CranioSynOstosis (CSO) is the premature fusion of one or more of cranial sutures that connect individual skull bones for kids. The estimated prevalence of CSO is one in 2500 live births and even higher. Our ultimate goal is to develop an open-source imaging-informatics-platform, eSuture, for clinicians to objectively classify the craniosynostosis using computed tomography (CT) data, and accurately estimate patient-specific spring force for spring-assisted surgery (SAS). CSO is an extremely serious birth defect that involves the premature fusion, of one or more sutures on a baby's skull. CSO, in terms of the fused suture, could be classified mainly into several types of synostosis, such as sagittal, coronal, metopic, and lambdoid. Infants with CSO may have problems with brain and skull growth, resulting in cognitive impairment. This defect may not only ruin the infant's life, but also deeply affect the infant's family. SAS, recognized as a safe, effective, and less invasive treatment method, introduced to treat the CSO. This treatment uses the force of a spring to reshape the skull in a slower manner that harnesses the growth of the skull to assist with shape change. Patient-specific spring selection is the principal barrier to the advancement of SAS for CSO because few surgeons have the experience to select personalized springs for each patient. The selection of the spring force is a crucial step in this surgical treatment, and it is dependent on the experience of the surgeon. Important factors essential in the selection of the spring force include the ages, bone thickness and the subtypes of CSO. One example is that sagittal CSO with an elongated occiput needs a stronger posterior spring, while one with no predominant characteristics typically needs a mid-range anterior and posterior spring. The current problem is that we do not have a complete objective way of classification of CSO and sagittal CSO and estimation of the spring force for the individual. Our hypothesis is that CSO and sagittal CSO can be accurately classified based on the features from sutures and head shape, and behaviors of calvarial bone tissue following virtual optimal spring force can be accurately simulated by integrating a finite element method (FEM) with statistical learning model. To test our hypothesis, we are proposing the following Specific Aim: (1) To define the eSuture Informatic system and build the database, with CT and DTI data; (2) To develop tools for image processing, segmentation, registration, quantification of sutures, and automatically categorize each patient to one catalogue of the CSO types or sagittal CSO subtype; (3) To model and estimate the optimal spring force for SAS; and (4) To validate and evaluate the eSuture system. Our system will produce a paradigm shift in CSO diagnosis and treatment.